Scalable Systems and Methods for Generating and Serving Recommendations

ABSTRACT

A scalable recommendation engine includes stateless processors in communication with at least one memory server that stores contextual data. A router is configured to direct a recommendation request to a first stateless processor, which is configured to generate a recommendation using contextual data from the memory server. A controller monitors the available processing bandwidth of the stateless processors and deploys an additional stateless processor if the available processing bandwidth is less than a minimum available processing bandwidth threshold. The controller can remove from deployment a stateless processor if the available processing bandwidth is greater than a maximum pre-determined available processing bandwidth threshold.

TECHNICAL FIELD

This application generally relates to computer systems and methods forgenerating and serving recommendations for content, products, and/orservices that are related to a user's online and/or offline activity.

BACKGROUND

Commercial websites and applications often provide recommendations totheir users. Such recommendations can include content related to thecurrent webpage or application accessed by the user (e.g., a relatednews story), a product related to a product in a user's shopping cart(e.g., a recommendation for socks if the user is buying shoes), or apromotion/advertisement related to the current webpage accessed by theuser and/or a product in a user's shopping cart. Product and offerrecommendations can also be injected into email communications sent tousers. Recommendations that are personalized, relevant, and appropriatecan help increase user traffic, sales, and/or revenue and, therefore,they are important components of commercial websites and applications.

In order to generate a relevant recommendation, a recommendation enginetakes into account one or more factors or data regarding the user and/orthe content of the current webpage accessed by the user. Generally, therecommendation engine uses real-time information as well as historicalinformation accumulated over large periods of time to generate therecommendation. Such a recommendation engine requires memory andprocessing power, which may vary depending on the volume of usertraffic, the number of products or offers in the merchant's catalog, andthe amount of historical data available. The recommendation engine alsorequires network bandwidth to serve the recommendation without anundesired latency delay.

FIG. 1 is a block diagram of a system 10 for providing recommendationsto a client 100 according to the prior art. As a user is viewing awebpage on the client 100 that is downloaded from a host 110, the host110 transmits a request for a recommendation to a recommendation backend120. The recommendation backend 120 includes a routing server 130 and aplurality of recommendation servers 140 a, 140 b, 140 c, 140 n. Therecommendation servers 140 a, 140 b, 140 c, 140 n each include a logicprocessor 150 a, 150 b, 150 c, 150 n, and a memory 160 a, 160 b, 160 c,160 n, respectively. Although the processors may vary betweenrecommendation servers, each memory is a mirror image of the othermemories. Each memory contains all the information that the respectiveserver needs to respond to a recommendation request.

The routing server 130 receives the recommendation request anddetermines which recommendation server 140 a, 140 b, 140 c, 140 n tosend the recommendation request to. The routing server 130 can takevarious factors into consideration to determine the appropriaterecommendation server 140 a, 140 b, 140 c, 140 n to handle therecommendation request, such as the available capacity and thegeographic location of each recommendation server. The routing server130 then transmits the recommendation request to the appropriaterecommendation server 140 a to process the request. Upon receiving therecommendation request, the processor 150 a queries the memory 160 a fordata relevant to the request. Such data can include personal informationregarding the user, information regarding the webpage or websiteaccessed by the user, and/or a list of products related to a product inthe user's shopping cart. The processor 150 a then applies logic (e.g.,a recommendation algorithm) to the data and returns a recommendation tothe routing server 130, which then transmits the recommendation toclient 100 via the host 110.

In response to the volume of recommendation requests (e.g., due toincreased or decreased website traffic), the routing server 130 canadjust the number of recommendation servers 140 a, 140 b, 140 c, 140 nupwards or downwards. If the routing server 130 needs to deploy a newrecommendation server 140 n in response to an increased volume ofrecommendation requests, the routing server 130 must first cause anexisting recommendation server 140 c to copy its memory 160 c to thememory 160 n of the new server 140 n. Since the memory 160 c is verylarge, it may take several hours or more to copy memory 160 c to memory160 n. Thus, it may take several hours or more to deploy the newrecommendation server 140 n. Also, some of the bandwidth of server 140 cis diverted to bring new server 140 n online. Since the backend 120 isovercapacity (or near overcapacity) until new server 140 n is broughtonline, the recommendation requests will take longer to process, whichresults in an undesired latency. For this reason, new recommendationservers are often deployed before their capacity is truly needed toallow for adequate time to copy the data to the new server. As a result,recommendation backends 120 generally operate at over capacity, whichresults in undesired costs and inefficiencies.

SUMMARY

The following description and drawings set forth certain illustrativeimplementations of the disclosure in detail, which are indicative ofseveral exemplary ways in which the various principles of the disclosuremay be carried out. The illustrative examples, however, are notexhaustive of the many possible embodiments of the disclosure. Otherobjects, advantages and novel features of the disclosure will be setforth in the following detailed description of the disclosure whenconsidered in conjunction with the drawings.

Aspects of the invention are directed to the use of stateless servers orprocessors, which generally do not retain (store) information regardingthe state of some or any transactions or conditions, e.g., session datafrom interactions with a client computer. A scalable recommendationengine includes stateless processors in communication with at least onememory server that stores contextual data. A router is configured todirect a recommendation request to a first stateless processor, which isconfigured to generate a recommendation using contextual data from thememory server. A controller monitors the available processing bandwidthof the stateless processors and deploys an additional statelessprocessor if the available processing bandwidth is less than a minimumavailable processing bandwidth threshold. The controller can remove fromdeployment a stateless processor if the available processing bandwidthis greater than a pre-set available processing bandwidth threshold,which can be referred to here as a maximum, pre-set maximum orpre-determined maximum bandwidth.

In an aspect, the invention includes a method of generating arecommendation for a user operating a client. The method comprises in arecommendation generation engine having an array of stateless processorsin communication with a memory, receiving a recommendation request froma host. The method also comprises in said recommendation generationengine, directing said recommendation request to a first statelessprocessor in said array of stateless processors. The method alsocomprises in said first stateless processor, receiving contextual datain response to a contextual data query to said memory, said contextualdata including a webpage currently accessed by said client. The methodalso comprises in said first stateless processor, generating saidrecommendation using said contextual data, said recommendation for arelated content of said webpage.

In another aspect, the invention includes a method of controlling aprocessing capacity of a recommendation engine comprising a plurality ofstateless processors and a plurality of memory servers. The methodcomprises monitoring an available processing bandwidth of saidrecommendation engine. The method also comprises determining if theavailable processing bandwidth is less than a minimum availableprocessing bandwidth threshold. The method also comprises activating anadditional stateless processor if the available processing bandwidth isless than the minimum available processing bandwidth threshold.

In another aspect, the invention includes a scalable recommendationengine. The recommendation engine comprises a plurality of statelessprocessors; at least one memory server in communication with theplurality of stateless processors, the memory server storing contextualdata; a router in communication with the plurality of statelessprocessors to direct a recommendation request to a first statelessprocessor, wherein the first stateless processor is configured togenerate a recommendation using said contextual data; and a controllerin communication with the plurality of processors, the controllerconfigured to monitor an available processing bandwidth of saidplurality of processors and to deploy an additional stateless processorif the available processing bandwidth is less than a minimum availableprocessing bandwidth threshold.

IN THE DRAWINGS

For a fuller understanding of the nature and advantages of the presentinvention, reference is made to the following detailed description ofpreferred embodiments and in connection with the accompanying drawings,in which:

FIG. 1 is a block diagram of a system for providing recommendations to aclient according to the prior art;

FIG. 2 is a block diagram of a system for providing recommendations to aclient according to an embodiment;

FIG. 3 is a flow chart of a method for generating a recommendationrequest according to an embodiment; and

FIG. 4 is a flow chart of a method for controlling the capacity of arecommendation engine according to an embodiment;

FIG. 5 is a block diagram of a system for providing recommendations to aclient according to an embodiment; and

FIG. 6 is a flow chart 60 for recommending a product to a user.

DETAILED DESCRIPTION

FIG. 2 is a block diagram of a system 20 for providing recommendationsto a client 200 according to an embodiment. The system 20 includes theclient 200, a host 210, and a recommendation engine 220. Therecommendation engine 220 includes a router server 230, a controller235, stateless logic processors 240 a, 240 b, 240 c, 240 n (generally240), and memory stores 250 a, 250 b, 250 c (generally 250). Thestateless logic processors 240 are servers or virtual servers thatprovide computing power for the recommendation engine 220. In someembodiments, each logic processor 240 is identical. The logic processors240 are stateless in that they do not permanently store the data neededto generate a recommendation. Instead, the data is stored in memorystores 250, which can be accessed by the stateless processors 240through a query.

Since the stateless logic processors 240 and memory stores 250 aredecoupled, a new stateless logic processor 240 n can be brought onlinequickly and independently because data does not need to be copied into anew memory store 250 n associated with the new stateless logic processor240 n. Also, since the logic processors 240 can be identical orsubstantially identical to one another, there is little configuration orlead time needed to bring a new stateless logic processor 240 online.For example the new stateless logic processor 240 only needs to load therecommendation service loaded and to retrieve configuration parameters(e.g., logic or rules for recommendations) prior to going online. Thus,the recommendation engine 220 can activate and deactivate the statelesslogic processors 240 in close to real time as the capacity needs of therecommendation engine 220 fluctuate, providing a highly scalablerecommendation engine 220. For example, a new stateless logic processor240 n can be activated quickly if the recommendation engine 220 receives(or expects to receive) a surge in recommendation requests. Also, anexisting stateless processor (e.g., 240 c) can be deactivated if thevolume of recommendation requests drops below a threshold value or thelogic processors 240 are operating at greater than a pre-set maximumcapacity (e.g., greater than 50% excess capacity). Since the logicprocessor (e.g., 240 c) can be re-activated in short order, there isminimal risk in deactivating the logic processor.

The memory stores 250 can include one or more databases (or othermethods of/structures for data storage) hosted in a cluster of on one ormore servers and/or virtual servers. In some embodiments, the memorystores are Redis data servers or data server instances. In someembodiments, the memory stores 250 can communicate with one another. Inaddition, the servers and/or virtual servers in a given cluster cancommunicate with each other, for example to shard data across thecluster. A new memory store 250 can be brought online when extra storagecapacity (e.g., for recommendation data) is needed and/or for geographicdistribution of the memory store 250 (e.g., adding a memory store 250 inEurope to service the European market). For example, additionalrecommendation data can be driven by new customers, new products, or thelike. Such a new memory store 250 can be brought online without havingto bring a new stateless processor 240 online.

In operation, a user accesses the client 200 to view a webpage or anemail promotional advertisement. The contents of the webpage aretransmitted to the client 200 from the host 210 over a first network(e.g., a first wide area network). In parallel (or at any time), thehost 210 and/or client 200 can send a request for a recommendation tothe recommendation engine 220. The recommendation request can includeinformation relating to the user's browsing activity, the user's accountinformation, the products in the user's shopping cart, the type ofdevice operated by the user, and/or the time and/or date that therecommendation request was generated. The user's browsing activity caninclude the advertisement, promotional email, or URL link (e.g., a linkfor an advertisement, a link for a product or service, etc.) that theuser accessed to generate the recommendation request. The request can besent over an API.

The recommendation request is sent over a second network (e.g., a secondwide area network), which can be the same or different than the firstnetwork. The router server 230 receives the recommendation request andselects a stateless logic processor 240 (e.g., stateless logic processor240 a) to process the recommendation request. The router server 230 canselect the stateless logic processor 240 based on the percentage of apre-set maximum capacity at which the stateless logic processor 240 isoperating, the geographic location of the stateless logic processor 240,and/or the network bandwidth for the stateless logic processor 240. Therouter server 230 then forwards the recommendation request to theappropriate stateless logic processor 240 (e.g., stateless processor 240a). In some embodiments, the router server 230 is in communication witha load balancer to balance the processing workload across the availablestateless processor 240.

The controller 235 is in communication with the router server 230 over anetwork (e.g., a local area network or a wide area network). In someembodiments, the controller 235 and router server 230 are on the sameserver or virtual server. The controller 235 is configured to determinethe available bandwidth of the stateless processors 240, for example bysending queries to each logic processor 240 via the router server 230.Alternatively, the available bandwidth of the stateless processors 240is measured directly and/or pushed to the controller 235. If theavailable processing bandwidth is less than a minimum pre-determinedthreshold value (e.g., 5%, 10%, or 20%), the controller 235 can deployadditional stateless processors 240 to increase the available processingbandwidth. In addition or in the alternative, the controller 235 candeploy additional stateless logic processors 240 in advance of ananticipated increase in recommendation requests even though theavailable processing bandwidth is greater than the minimum thresholdvalue described above. For example, the controller 235 can deployadditional stateless logic processors 240 in advance of an emailpromotional campaign or due to other expected increases inrecommendation request traffic (e.g., on Black Friday). Likewise, thecontroller 235 can decrease the number of stateless processors 240 ifthe available processing bandwidth is greater than a maximumpre-determined threshold value (e.g., 25%, 30%, or 35%). For example,the controller 235 can first increase the number of stateless logicprocessors 240 in advance of an email campaign and then decrease thenumber of stateless logic processors 240 after the associated surge inrecommendation requests has ended. In another example, the controller235 increases the number of processors 240 during the day and decreasesthe number of processors 240 during the night. Deployment of additionalprocessors 240 can include allocating additional processors 240 (e.g.,from a service provider such as Amazon Web Services) and configuring theprocessors 240 with the recommendation service including algorithms (asdescribed herein) for providing recommendations and related businesslogic. In some embodiments, the processors 240 periodically fetch thealgorithms and business logic from the advertiser/retailer/serviceprovider to make sure they are up to date.

The stateless logic processor 240 evaluates the information (if any)associated with the recommendation request and determines whatcontextual data to request from the memory store 250. The processor 240then sends a query to an available memory store 250 (e.g., memory store250 b) for the appropriate contextual data. In some embodiments, thestateless logic processor 240 sends the query to multiple memory stores250. The logic processor 240 can use the data from the first memorystore 250 to respond to the query, or the logic processor 240 can useall responses. The contextual data can include the user's accountnumber, the user's geographic location, the user's gender, the user'sage, the user's website access history, the user's order history, theuser's social media posts (or other social media interaction data), thecontents of the webpage being accessed by the user, product reviews,product inventory, product pricing and sales, the product catalog, thepopularity of each product, product ordering trends, the weatherforecast, and similar information.

After the logic processor 240 receives the contextual data from thememory store 250 and stores the contextual data in a local memory orcache, the logic processor 240 applies an algorithm or other logic usingat least some of the contextual data and the information received withthe recommendation request to generate a relevant recommendation. Atthis stage, the logic processor 240 can apply any merchant-definedbusiness rules to filter/influence the recommendations shown to theuser. For example, the merchant might decide not to show any products onclearance to the user. Another example might be that the merchant mightdecide which brands are eligible to be shown to a user depending on thebrand of the current product the user is browsing. Another example mightbe that the merchant prefers products with certain attributes (e.g.,color, fit, style, etc.) over others to be shown as recommendations. Thealgorithms and business rules are processed and applied by logicprocessor 240 to generate a recommendation. The processor 240 thentransmits the recommendation to the router server 230, which forwardsthe recommendation to the host 210 to provide to the client 200. In someembodiments, different algorithms can be applied to similar users, forexample, to test the relative performance of the algorithms.

The recommendation can be for related content (e.g., a related newsstory) selected just for that particular user, a related product orservice (e.g., tennis balls if the user is buying a tennis racket)selected just for that particular user, a promotion (e.g., a sale formen's clothing if the user is a male) selected just for that particularuser, or a similar recommendation as understood by those skilled in theart.

In some embodiments, the router server 230 can send the recommendationrequest to multiple stateless processors 240 in order to obtain multiplerecommendations based on different algorithms, and the results of therecommendations requests can be combined or merged together. Forexample, logic processor 240 a may apply an algorithm based on theuser's gender and logic processor 240 b may apply an algorithm based onthe products in the user's shopping cart. The router server 230 or athird logic processor 240 c can merge the two recommendations into asingle recommendation for the user. Alternatively, the router server 230or third logic processor 240 c can provide both recommendations to theuser. In some embodiments, the configuration parameters retrieved fromthe merchant can determine whether a recommendation request is sent tomultiple processors 250.

While operation of the recommendation engine 220 has been described withrespect to a webpage or email, the recommendation engine 220 is notlimited to these technologies. The recommendation engine 220 can beapplied to any technology in which a recommendation is desired. Forexample, the recommendation engine 220 can be applied to applications,video games, online dating, smart phones, in-store advertisements,coupons (online or in-store printed at checkout), vending machines,e-book readers, or restaurants (e.g., menu recommendations).

In some embodiments, the recommendation engine 220 can be described ashaving an outer layer 245 of stateless logic processors 240 and an innerlayer 255 of memory stores 250. The number of stateless logic processors240 in the outer layer 245 can be adjusted upwards or downwards based onthe capacity needs of the recommendation engine 220, the volume ofrecommendation requests received by the recommendation engine 220,and/or the network traffic. The stateless logic processors 240 in theouter layer 245 communicate with the memory stores 250 in the innerlayer 255 to access contextual data for generating the recommendations.

FIG. 3 is a flow chart of a method 30 for generating a recommendationrequest according to an embodiment. The method 30 includes 300 receivinga recommendation request from a host. As discussed above, therecommendation request can include information relating to the user'sbrowsing activity, the user's account information, the products in theuser's shopping cart, the type of device operated by the user, and/orthe time and/or date that the recommendation request was generated. Themethod also includes 310 directing the recommendation request to a firststateless logic processor. The recommendation request can be directed tothe first stateless logic processor by a router or a router server asdiscussed above. The method also includes 320 receiving (e.g., at astateless logic processor) contextual data in response to a query to amemory store. The method also includes 330 generating (e.g., at astateless logic processor) a recommendation request using at least someof the contextual data.

FIG. 4 is a flow chart of a method 40 for dynamically controlling thecapacity of a recommendation engine. The method 40 includes 400determining the available processing bandwidth of the stateless logicprocessors (e.g., stateless logic processors 240). At 410, the availableprocessing bandwidth is compared to a minimum available processingbandwidth threshold. The minimum available processing bandwidththreshold can be set manually by an operator (e.g., through anadministrator user interface) or it can be set automatically based onhistorical data or best practices. The minimum available processingbandwidth threshold can be 5%, 10%, 15%, 20%, or any value therebetween.If the available processing bandwidth is less than the minimum availableprocessing bandwidth threshold, at 420 one or more additional statelesslogic processors are deployed to increase the available processingbandwidth of the recommendation engine. In some embodiments, additionalstateless logic processors are deployed iteratively. For example, afirst additional stateless processors can be deployed and then theavailable processing bandwidth can be determined. If the availableprocessing bandwidth is still less than the minimum processing bandwidththreshold, a second additional logic stateless processor can be deployedand then the available processing bandwidth can be determined again.This iterative process can continue until the available processingbandwidth is greater than the minimum available processing bandwidththreshold.

If the available processing bandwidth is not less than the minimumavailable processing bandwidth threshold, the method proceeds to 430. At430, the available processing bandwidth is compared to the pre-setmaximum available processing bandwidth threshold. The pre-set maximumavailable processing bandwidth threshold can be set manually by anoperator (e.g., through an administrator user interface) or it can beset automatically based on historical data or best practices. Thepre-set maximum available processing bandwidth threshold can be 40%,45%, 50%, or any value therebetween. If the available processingbandwidth is greater than the pre-set maximum available processingbandwidth threshold, one or more excess stateless processors arereleased from deployment to reduce the excess processing bandwidth ofthe recommendation engine. In some embodiments, excess stateless logicprocessors can be released from deployment iteratively. For example, afirst excess stateless logic processor can be released from deploymentand then the available processing bandwidth can be determined. If theavailable processing bandwidth is still greater than the pre-set maximumavailable processing bandwidth threshold, a second excess statelesslogic processor can be released from deployment and then the availableprocessing bandwidth can be determined again. This iterative process cancontinue until the available processing bandwidth is less than thepre-set maximum available processing bandwidth threshold. In thesesections, processing bandwidth may refer to CPU usage, memory usage,network usage, or a combination of these factors.

FIG. 5 is a block diagram of a system 50 for providing recommendationsto a client 500 according to another embodiment. The system 50 includesa host 510, a router 520, and a regional recommendation engine 525. Theregional recommendation engine 525 includes a load balancer 530,stateless logic processors 540A, 540B, 540C (generally 540), a productserver 550, a cache server 550, logging servers 555, pre-computedrecommendation servers 560, real-time model scoring servers 565, andanalytics servers 570. The regional recommendation engine 525 representsone of a plurality of recommendation engines that can be geographicallydistributed to provide fast response times.

Similar to the embodiments discussed above, the client 500 is incommunication with the host 510, for example by accessing a website oremail server hosted by the host 510. The client 500 or host 510 sends arequest for a recommendation, which is transmitted to the router 520.The router 520 directs the recommendation request to regionalrecommendation engine 525. The regional recommendation engine 525 can beselected based on the relative locations of the client 500 and therecommendation engine 525 and/or the available processing bandwidth ofthe recommendation engine 525. The router 520 can be a DNS latencyrouter.

The recommendation request is sent to a load balancer 530, which directsthe request to a stateless logic processor cluster or layer 545. Theload balancer 530 determines the available processing bandwidth of eachstateless logic processor 540 in the stateless logic processorcluster/layer 545 and directs the recommendation request to thestateless logic processor 540 with the most available processingbandwidth to distribute the processing load across the statelessprocessors 540.

The stateless processors 540 are in communication with product server550, cache server 555, logging servers 560, pre-computed recommendationservers 565, real-time model scoring servers 570, and analytics servers575. The stateless processor 540 can query or call the pre-computedrecommendation servers 565 for one or more pre-calculatedrecommendations. The pre-calculated recommendations can include the mostpopular products overall, the most popular products for customers fromthe same city or region, products that are commonly purchased together(e.g., shirts and ties), popular products for the current season,products on sale, etc. Pre-calculated recommendations can also includerecommendations based on historical online activity and/or purchases bythe customer. In addition, the stateless processor 540 can query or callthe real-time model scoring servers 570 for one or more real-timerecommendations based on the user's online activity. Examples ofreal-time recommendations include recommendations based on items in theuser's shopping cart, recommendations based on the user's browsinghistory within the retailer's website, and recommendations based on theuser's online activity generally (e.g., social media posts, websearches, etc.).

After the stateless processor 540 receives the recommendations from thepre-computed recommendation servers 565 and/or the real-time modelscoring servers 570, the stateless processor 540 can communicate withthe product server 550. For example, the product server 550 has adatabase of the product catalog offered by the retailer. Thus, thestateless processor 540 can query the product server 550 for the currentproduct offerings that match the recommended products, such as theactual ties currently available in response to a recommendation forties. The product server 550 also includes various metadata about eachproduct, such as its price, whether it's on sale, whether it's a newproduct or a product near the end of its lifecycle, the inventory of theproduct, the season for which the product is intended (e.g., heavy coatsfor winter), etc. The stateless processor 540 can use the productmetadata to select additional recommended products based onrecommendations from pre-computed recommendation servers 565 and/or fromreal-time model scoring servers 570. The stateless processor 540 cantemporarily store data in cache server 555 while processing therecommendation request. For example, the cache server 555 can storeintermediate and/or final calculations from stateless processor 540 tospeed up subsequent responses.

The logging servers 560 can log the recommendation requests and therecommendations processed by the stateless processor 540 for lateranalysis. The analytics servers 575 can track user activity on retailerwebsites and compute certain metrics for later use. For example, theanalytics servers 575 can be used to track the conversion rate (sales)of the recommended products and the methodology used to generate therecommendations.

FIG. 6 is a flow chart 60 for recommending a product to a user. In step610, the stateless processor determines whether the user is known to themerchant. For example, the stateless processor can query a user databasebased on the user's email address, the user's IP address, data in acookie on the user's device, etc. If the user is not known to themerchant, in step 620 the recommendation engine generates a preliminaryrecommendation based on aggregate sales data. For example, therecommendation engine can generate a preliminary recommendation based onthe top products sold to users that have the same device or that live inthe same location as the user for which the recommendation is intended.If the user is known to the merchant, in step 630 the recommendationengine determines whether it has any data on recent activity (e.g.,browsing activity, products in shopping cart, etc.) for the user. Ifthere is data for recent activity, the recommendation engine applies arecent activity machine learning model 640 to generate a preliminaryrecommendation. The recent machine learning model 640 uses only veryrecent activity to generate the preliminary recommendations. If there isno data for recent activity, the recommendation engine applies ahistoric activity machine learning model 650 to generate a preliminaryrecommendation. The historic activity machine learning model 650 usesall available historical data for the user to generate the preliminaryrecommendations. In some embodiments, both the recommendation engineapplies both the recent activity machine learning model 640 and thehistoric activity machine learning model 650.

The output of the recent activity machine learning model 640 and/or thehistoric activity machine learning model 650 is merged at step 660(though no merger is necessary if only one of models 640 and 650 isapplied). The output of step 660 is combined with the output of step 620at step 670 to provide a combined preliminary recommendation output. Inother words, the preliminary recommendations generated at step 620(unknown user model) are merged with the preliminary recommendationsfrom steps 640 and/or 650 (known user models) to provide a superset ofpreliminary recommendations. At step 680, business rules are applied tothe combined preliminary recommendation output to generate a finalrecommendation, which is returned to the host/user at step 690. Thebusiness rules applied in step 680 can include which products are onsale, which products the merchant wants to promote, which products arein season, etc., as discussed above.

As would now be appreciated, the above systems and methods allow for thedynamic increase or decrease in processing bandwidth of a recommendationengine. Such systems and methods can reduce the undesired processinglatency in recommendation engines that operate at overcapacity whileadditional processing power is brought online. The systems and methodscan also provide cost savings because the recommendation engine does notneed to operate at overcapacity since additional processing power can bedeployed dynamically. As such, the excess processing power can bereleased for more productive use within an enterprise or released backto the cloud provider (e.g., Amazon).

The present invention should not be considered limited to the particularembodiments described above, but rather should be understood to coverall aspects of the invention as fairly set out in the present claims.Various modifications, equivalent processes, as well as numerousstructures to which the present invention may be applicable, will bereadily apparent to those skilled in the art to which the presentinvention is directed upon review of the present disclosure. The claimsare intended to cover such modifications.

What is claimed is:
 1. A method of generating a recommendation for auser operating a client, the method comprising: in a recommendationgeneration engine having an array of stateless processors incommunication with a memory, receiving a recommendation request from ahost; in said recommendation generation engine, directing saidrecommendation request to a first stateless processor in said array ofstateless processors; in said first stateless processor, receivingcontextual data in response to a contextual data query to said memory,said contextual data including a webpage currently accessed by saidclient; and in said first stateless processor, generating saidrecommendation using said contextual data, said recommendation for arelated content of said webpage.
 2. The method of claim 1, furthercomprising transmitting said recommendation to said host.
 3. The methodof claim 1, wherein the contextual data includes personal informationregarding the user.
 4. The method of claim 3, wherein the personalinformation includes identity information for the user.
 5. The method ofclaim 1, wherein the contextual data includes demographic informationregarding the user.
 6. The method of claim 1, wherein the contextualdata includes a browsing history of said user.
 7. The method of claim 1,wherein the recommendation is for a recommended product or recommendedservice.
 8. The method of claim 7, wherein the related content includesa shopping cart having a product that the user intends to purchase. 9.The method of claim 1, further comprising monitoring an availableprocessing bandwidth of said recommendation engine.
 10. The method ofclaim 9, further comprising activating an additional stateless processorif the available processing bandwidth is less than a minimum availableprocessing bandwidth threshold.
 11. The method of claim 9, furthercomprising deactivating an excess stateless processor if the availableprocessing bandwidth is greater than a pre-determined maximum availableprocessing bandwidth threshold.
 12. A method of controlling a processingcapacity of a recommendation engine comprising a plurality of statelessprocessors and a plurality of memory servers, the method comprising:monitoring an available processing bandwidth of said recommendationengine; determining if the available processing bandwidth is less than aminimum available processing bandwidth threshold; and activating anadditional stateless processor if the available processing bandwidth isless than the minimum available processing bandwidth threshold.
 13. Themethod of claim 13, further comprising: determining if the availableprocessing bandwidth is greater than a pre-determined availableprocessing bandwidth threshold; and deactivating an excess statelessprocessor if the available processing bandwidth is greater than thepre-determined maximum available processing bandwidth threshold.
 14. Themethod of claim 12, further comprising activating the additionalstateless processor in advance of an email promotion even if theavailable processing bandwidth is greater than the minimum availableprocessing bandwidth threshold.
 15. A scalable recommendation enginecomprising: a plurality of stateless processors; at least one memoryserver in communication with the plurality of stateless processors, thememory server storing contextual data; a router in communication withthe plurality of stateless processors to direct a recommendation requestto a first stateless processor, wherein the first stateless processor isconfigured to generate a recommendation using said contextual data; anda controller in communication with the plurality of processors, thecontroller configured to monitor an available processing bandwidth ofsaid plurality of processors and to deploy an additional statelessprocessor if the available processing bandwidth is less than a minimumavailable processing bandwidth threshold.
 16. The scalablerecommendation engine of claim 15, wherein the controller is configuredto release an excess stateless processor from deployment if theavailable processing bandwidth is greater than a pre-determined maximumavailable processing bandwidth threshold.